Many works have been developed to achieve fairness in machine learning community [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. A pilot study on fair node representation learning is developed based on random walk [7]}. Additionally, adversarial debiasing is adopt to learn fair prediction or node representation so that the well-trained adversary can not predict the sensitvie attribute based on node representation or prediction [8]}, [9]}, [10]}. A Bayesian approach is developed to learn fair node representation via encoding sensitive information in prior distribution in [11]}. Work [12]} develops a PAC-Bayesian analysis to connect subgroup generalziation with accuracy parity. [13]}, [14]} aim to mitigate prediction bias for link prediction. Fairness-aware graph contrastive learning are proposed in [15]}, [16]}. However, aforementioned works ignore the role of graph topology information in terms of fairness. In this work, we theoretically and experimentally reveal that many GNNs aggregation schemes boost node representation bias under topology bias. Furthermore, we develop a efficient and transparent fair message passing scheme with theoretical guarantee utilizing useful information of neighbors and debiasing node representation simultaneously.
